arXiv Machine Learning
Jun 26
A Causal Foundation Model for Structure and Outcome Prediction
arXiv:2606. 26467v1 Announce Type: new Abstract: We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems.
A Causal Foundation Model for Structure and Outcome Prediction
We introduce TabPFN-CFM, a causal foundation model that can handle multiple causal problems. TabPFN-CFM predicts both causal structure and outcomes from observational data, supports queries on all three levels of Pearl's Causal Hierarchy and uses known graph structure when available to improve predictions.
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arXiv Machine Learning
Jun 26
Use What You Know: Causal Foundation Models with Partial Graphs
arXiv:2602. 14972v2 Announce Type: replace Abstract: Estimating causal quantities traditionally relies on bespoke estimators tailored to specific assumptions.
Hugging Face Trending Papers
Sep 10
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
CausalArena is a unified, evolvable benchmark designed to evaluate causal discovery methods across diverse structural causal models (SCMs). It incorporates synthetic SCMs for controlled structural variation, semantic operational SCMs for human-auditable environments, and formula-grounded SCMs to test discovery under explicit scientific mechanisms, along with real-world datasets for external validity. Experiments show that performance rankings vary significantly across SCM families and protocols, indicating that strong results on one benchmark do not generalize to others, especially in the context of causal discovery foundation models.
arXiv Machine Learning
Sep 11
CausalArena: Benchmarking Causal Discovery in the Foundation Model Era
CausalArena is a new benchmark designed to evaluate causal discovery methods in the era of foundation models. It unifies synthetic structural causal models (SCMs), semantically grounded SCMs, and formula‑grounded SCMs, while also including real‑world datasets for external validation. Experiments show that performance rankings vary widely across different SCM families and protocols, indicating that strong results on one benchmark do not necessarily transfer to others.
arXiv Machine Learning
Sep 4
Causal Foundation Models
Causal Foundation Models (CFMs) are pretrained neural networks designed to estimate causal quantities—such as the average treatment effect—across new datasets using in‑context learning, eliminating the need for bespoke pipelines or model updates. The paper introduces CFMs, reviews foundational concepts in causal inference and machine learning, and provides practical code examples and Jupyter notebooks to illustrate their application.
arXiv Machine Learning
4d ago
What You Observe Determines How You Identify Causal Effects: Evaluating Causal Models across Observational Views
arXiv:2609.36881v1 Announce Type: new Abstract: Causal foundation models (CFMs) pre-trained on data generated from various structural causal models (SCMs) have been proposed for estimating causal eff...
arXiv Machine Learning
Jul 8
Improving TabPFN's Synthetic Data Generation by Integrating Causal Structure
arXiv:2603. 10254v2 Announce Type: replace Abstract: Synthetic tabular data generation addresses data scarcity and privacy constraints in a variety of domains.
arXiv Machine Learning
2d ago
CIDER-FM: Foundation Models for Causal Inference from Diverse Experimental Regimes
CIDER-FM is a causal foundation model that combines finite observational data with surrogate-interventional datasets to predict target conditional interventional distributions more accurately than using observational data alone. It employs an intervention-aware representation and hierarchical three‑axis attention to integrate information across variables, samples, and experimental regimes. Experiments on synthetic graphs, simulated data, and real‑world Causal Chambers data show that incorporating experimental context improves CID prediction performance.
arXiv Machine Learning
Jul 14
DAG-FM: A Foundation Model for Causal Discovery under Heterogeneous Causal Mechanisms
arXiv:2607. 11510v1 Announce Type: new Abstract: Causal discovery from observational tabular data remains fundamentally challenging, primarily due to the heterogeneity of underlying causal mechanisms and the high-dimensional combinatorial search space of Directed Acyclic Graphs (DAGs).
arXiv Machine Learning
Jun 5
Causal Atlases from Entropic Inference: Bayesian Networks beyond Optimal DAGs
arXiv:2606. 06440v1 Announce Type: new Abstract: Data-driven causal relationship identification is pertinent to advancing understanding of complex systems both within and beyond science.
arXiv Machine Learning
Jun 19
Unsupervised Causal Abstractions Discovery
arXiv:2606. 19594v1 Announce Type: new Abstract: Causal abstractions formalize when a high-level structural causal model (SCM) captures the interventional behavior of a lower-level SCM.
arXiv AI
Sep 16
LimiX-2: A Contextual Mechanism Network Towards General Structured-Data Intelligence
arXiv:2609. 17488v1 Announce Type: new Abstract: We introduce LimiX-2, a new model in the LimiX family, developed through model and data scaling guided by our previously established scaling laws.